Experimental Investigation and Multi‐Response Optimization of Crashworthiness Performance in 3D ‐Printed Thin‐Walled Tubes

ABSTRACT This study investigates how geometric configurations and manufacturing parameters affect the crashworthiness performance of tubes printed by fused deposition modeling (FDM) using polymer filaments. Three distinct cross‐sectional geometries are evaluated using the standard polylactic acid (PLA), PLA PRO1, and carbon‐fiber‐reinforced polylactic acid (PLA‐CF) filaments. To optimize experimental efficiency, a Taguchi L9 orthogonal array is employed to analyze the interactions between material type, sectional geometry, wall thickness, and infill density. Mechanical characterization uses quasi‐static axial compression tests to evaluate deformation behavior and structural stability. The results demonstrate that sectional geometry is the overwhelmingly dominant factor, accounting for 68% to 88% of the total variance across the initial peak crushing force (IPCF), mean crushing force (MCF), specific energy absorption (SEA), and crushing force efficiency (CFE) ( p < 0.05). Specifically, hexagonal and circular topologies exhibited stable, progressive folding mechanisms, significantly outperforming square designs, which were prone to unstable buckling. Material selection emerged as the second most influential factor, contributing up to 27% of the variance in weight‐specific energy absorption and 17% in peak crushing force, with PLA‐CF providing superior weight‐specific energy dissipation. The optimization analysis identifies the hexagonal cross‐section printed with PLA‐CF as the ideal configuration for high‐performance energy absorbers. This research offers a systematic foundation for identifying optimal configurations to maximize energy absorption while ensuring structural integrity in thin‐walled polymeric structures. A confirmation experiment on the predicted optimum verifies the dominant role of the hexagonal geometry and PLA‐CF material and quantifies the uncertainty of the additive model at untested factor combinations.